Overview

Dataset statistics

Number of variables34
Number of observations34393
Missing cells26727
Missing cells (%)2.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.9 MiB
Average record size in memory272.0 B

Variable types

Numeric21
Categorical7
Text5
DateTime1

Alerts

Bleaching_indicator is highly overall correlated with Data_Source and 1 other fieldsHigh correlation
Data_Source is highly overall correlated with Bleaching_indicator and 4 other fieldsHigh correlation
Date_Year is highly overall correlated with Data_Source and 1 other fieldsHigh correlation
Exposure is highly overall correlated with Exposure_catHigh correlation
Exposure_cat is highly overall correlated with ExposureHigh correlation
Latitude_Degrees is highly overall correlated with Realm_NameHigh correlation
Longitude_Degrees is highly overall correlated with Ocean_Name and 1 other fieldsHigh correlation
Ocean_Name is highly overall correlated with Longitude_Degrees and 1 other fieldsHigh correlation
Percent_Bleaching is highly overall correlated with Bleaching_indicator and 1 other fieldsHigh correlation
Realm_Name is highly overall correlated with Latitude_Degrees and 2 other fieldsHigh correlation
SSTA is highly overall correlated with TSAHigh correlation
SSTA_DHW is highly overall correlated with SSTA_Frequency and 1 other fieldsHigh correlation
SSTA_Frequency is highly overall correlated with SSTA_DHW and 1 other fieldsHigh correlation
Sample_ID is highly overall correlated with Data_Source and 2 other fieldsHigh correlation
Site_ID is highly overall correlated with Substrate_NameHigh correlation
Substrate_Name is highly overall correlated with Data_Source and 2 other fieldsHigh correlation
TSA is highly overall correlated with SSTA and 1 other fieldsHigh correlation
TSA_DHW is highly overall correlated with SSTA_DHW and 1 other fieldsHigh correlation
TSA_Frequency is highly overall correlated with SSTA_Frequency and 1 other fieldsHigh correlation
Temperature_C is highly overall correlated with TSAHigh correlation
Data_Source is highly imbalanced (50.2%)Imbalance
Reef_ID has 11972 (34.8%) missing valuesMissing
City_Town_Name has 970 (2.8%) missing valuesMissing
Depth_m has 1677 (4.9%) missing valuesMissing
Substrate_Name has 12035 (35.0%) missing valuesMissing
Percent_Bleaching has 16553 (48.1%) zerosZeros
SSTA_Frequency has 1651 (4.8%) zerosZeros
SSTA_DHW has 12022 (35.0%) zerosZeros
TSA_Frequency has 11111 (32.3%) zerosZeros
TSA_DHW has 23322 (67.8%) zerosZeros

Reproduction

Analysis started2024-06-27 16:24:00.326107
Analysis finished2024-06-27 16:24:25.438580
Duration25.11 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Site_ID
Real number (ℝ)

HIGH CORRELATION 

Distinct11047
Distinct (%)32.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88533.562
Minimum1
Maximum1000060
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size268.8 KiB
2024-06-27T12:24:25.480297image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile754.6
Q13504
median5972
Q38554
95-th percentile999677
Maximum1000060
Range1000059
Interquartile range (IQR)5050

Descriptive statistics

Standard deviation274289.19
Coefficient of variation (CV)3.098138
Kurtosis7.126568
Mean88533.562
Median Absolute Deviation (MAD)2515
Skewness3.0205811
Sum3.0449348 × 109
Variance7.5234562 × 1010
MonotonicityNot monotonic
2024-06-27T12:24:25.769589image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3594 90
 
0.3%
3886 80
 
0.2%
3031 68
 
0.2%
8238 68
 
0.2%
3015 66
 
0.2%
3579 60
 
0.2%
3577 58
 
0.2%
3580 58
 
0.2%
3582 58
 
0.2%
3568 56
 
0.2%
Other values (11037) 33731
98.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 7
 
< 0.1%
4 22
0.1%
6 8
 
< 0.1%
7 4
 
< 0.1%
8 15
< 0.1%
9 6
 
< 0.1%
10 2
 
< 0.1%
11 2
 
< 0.1%
12 20
0.1%
ValueCountFrequency (%)
1000060 1
 
< 0.1%
1000059 1
 
< 0.1%
1000058 1
 
< 0.1%
1000057 1
 
< 0.1%
1000056 1
 
< 0.1%
1000055 1
 
< 0.1%
1000054 1
 
< 0.1%
1000053 1
 
< 0.1%
1000052 1
 
< 0.1%
1000042 4
< 0.1%

Sample_ID
Real number (ℝ)

HIGH CORRELATION 

Distinct23204
Distinct (%)67.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10094554
Minimum9623
Maximum10331713
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size268.8 KiB
2024-06-27T12:24:25.817582image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum9623
5-th percentile10275479
Q110311019
median10316417
Q310322107
95-th percentile10329989
Maximum10331713
Range10322090
Interquartile range (IQR)11088

Descriptive statistics

Standard deviation1491187.4
Coefficient of variation (CV)0.14772197
Kurtosis41.751239
Mean10094554
Median Absolute Deviation (MAD)5515
Skewness-6.6140262
Sum3.47182 × 1011
Variance2.22364 × 1012
MonotonicityNot monotonic
2024-06-27T12:24:25.865071image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10290623 4
 
< 0.1%
10308451 2
 
< 0.1%
10308421 2
 
< 0.1%
10308422 2
 
< 0.1%
10308460 2
 
< 0.1%
10308461 2
 
< 0.1%
10308462 2
 
< 0.1%
10308463 2
 
< 0.1%
10308437 2
 
< 0.1%
10308438 2
 
< 0.1%
Other values (23194) 34371
99.9%
ValueCountFrequency (%)
9623 1
< 0.1%
9624 1
< 0.1%
9625 1
< 0.1%
9626 1
< 0.1%
9627 1
< 0.1%
9628 1
< 0.1%
9629 1
< 0.1%
9630 1
< 0.1%
9631 1
< 0.1%
9632 1
< 0.1%
ValueCountFrequency (%)
10331713 1
< 0.1%
10331712 1
< 0.1%
10331711 1
< 0.1%
10331710 1
< 0.1%
10331709 1
< 0.1%
10331708 1
< 0.1%
10331707 1
< 0.1%
10331706 1
< 0.1%
10331705 1
< 0.1%
10331704 1
< 0.1%

Data_Source
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size268.8 KiB
Reef_Check
22421 
Donner
5759 
AGRRA
2848 
FRRP
2394 
Kumagai
 
660
Other values (4)
 
311

Length

Max length10
Median length10
Mean length8.4314541
Min length4

Characters and Unicode

Total characters289983
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDonner
2nd rowDonner
3rd rowDonner
4th rowDonner
5th rowDonner

Common Values

ValueCountFrequency (%)
Reef_Check 22421
65.2%
Donner 5759
 
16.7%
AGRRA 2848
 
8.3%
FRRP 2394
 
7.0%
Kumagai 660
 
1.9%
McClanahan 226
 
0.7%
Safaie 76
 
0.2%
Nuryana 5
 
< 0.1%
Setiawan 4
 
< 0.1%

Length

2024-06-27T12:24:25.905600image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-27T12:24:25.954764image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
reef_check 22421
65.2%
donner 5759
 
16.7%
agrra 2848
 
8.3%
frrp 2394
 
7.0%
kumagai 660
 
1.9%
mcclanahan 226
 
0.7%
safaie 76
 
0.2%
nuryana 5
 
< 0.1%
setiawan 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 73102
25.2%
R 32905
11.3%
C 22647
 
7.8%
h 22647
 
7.8%
c 22647
 
7.8%
f 22497
 
7.8%
_ 22421
 
7.7%
k 22421
 
7.7%
n 11979
 
4.1%
r 5764
 
2.0%
Other values (19) 30953
10.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 191948
66.2%
Uppercase Letter 75614
 
26.1%
Connector Punctuation 22421
 
7.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 73102
38.1%
h 22647
 
11.8%
c 22647
 
11.8%
f 22497
 
11.7%
k 22421
 
11.7%
n 11979
 
6.2%
r 5764
 
3.0%
o 5759
 
3.0%
a 2168
 
1.1%
i 740
 
0.4%
Other values (7) 2224
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
R 32905
43.5%
C 22647
30.0%
D 5759
 
7.6%
A 5696
 
7.5%
G 2848
 
3.8%
P 2394
 
3.2%
F 2394
 
3.2%
K 660
 
0.9%
M 226
 
0.3%
S 80
 
0.1%
Connector Punctuation
ValueCountFrequency (%)
_ 22421
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 267562
92.3%
Common 22421
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 73102
27.3%
R 32905
12.3%
C 22647
 
8.5%
h 22647
 
8.5%
c 22647
 
8.5%
f 22497
 
8.4%
k 22421
 
8.4%
n 11979
 
4.5%
r 5764
 
2.2%
D 5759
 
2.2%
Other values (18) 25194
 
9.4%
Common
ValueCountFrequency (%)
_ 22421
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 289983
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 73102
25.2%
R 32905
11.3%
C 22647
 
7.8%
h 22647
 
7.8%
c 22647
 
7.8%
f 22497
 
7.8%
_ 22421
 
7.7%
k 22421
 
7.7%
n 11979
 
4.1%
r 5764
 
2.0%
Other values (19) 30953
10.7%

Latitude_Degrees
Real number (ℝ)

HIGH CORRELATION 

Distinct9729
Distinct (%)28.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0320593
Minimum-28.8645
Maximum36.75
Zeros0
Zeros (%)0.0%
Negative8866
Negative (%)25.8%
Memory size268.8 KiB
2024-06-27T12:24:26.002316image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-28.8645
5-th percentile-20.7847
Q1-3.8532
median11.3003
Q320.4402
95-th percentile26.25864
Maximum36.75
Range65.6145
Interquartile range (IQR)24.2934

Descriptive statistics

Standard deviation15.791164
Coefficient of variation (CV)1.9660168
Kurtosis-0.7865087
Mean8.0320593
Median Absolute Deviation (MAD)9.8703
Skewness-0.64387534
Sum276246.61
Variance249.36085
MonotonicityNot monotonic
2024-06-27T12:24:26.046619image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-16.4994 90
 
0.3%
-16.5353 82
 
0.2%
-8.3376 80
 
0.2%
18.707 74
 
0.2%
12.2186 74
 
0.2%
28.5575 68
 
0.2%
10.1228 68
 
0.2%
28.437 66
 
0.2%
-16.5424 60
 
0.2%
18.724 59
 
0.2%
Other values (9719) 33672
97.9%
ValueCountFrequency (%)
-28.8645 2
 
< 0.1%
-28.7001 1
 
< 0.1%
-28.6591 1
 
< 0.1%
-28.4643 1
 
< 0.1%
-28.1625 2
 
< 0.1%
-28.1092 16
< 0.1%
-27.9867 2
 
< 0.1%
-27.9397 20
0.1%
-27.9369 6
 
< 0.1%
-27.9067 2
 
< 0.1%
ValueCountFrequency (%)
36.75 1
< 0.1%
35.7446 1
< 0.1%
35.3056 1
< 0.1%
35.2411 1
< 0.1%
35.2344 1
< 0.1%
35.1967 1
< 0.1%
35.1966 1
< 0.1%
35.1946 1
< 0.1%
35.1368 1
< 0.1%
35.1354 1
< 0.1%

Longitude_Degrees
Real number (ℝ)

HIGH CORRELATION 

Distinct9650
Distinct (%)28.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.424467
Minimum-179.8594
Maximum179.9645
Zeros0
Zeros (%)0.0%
Negative15035
Negative (%)43.7%
Memory size268.8 KiB
2024-06-27T12:24:26.093999image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-179.8594
5-th percentile-88.5104
Q1-80.0894
median56.3639
Q3119.7991
95-th percentile164.9806
Maximum179.9645
Range359.8239
Interquartile range (IQR)199.8885

Descriptive statistics

Standard deviation104.57741
Coefficient of variation (CV)3.8132886
Kurtosis-1.6174055
Mean27.424467
Median Absolute Deviation (MAD)96.7878
Skewness-0.16223223
Sum943209.68
Variance10936.434
MonotonicityNot monotonic
2024-06-27T12:24:26.136591image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99.835 120
 
0.3%
-151.784 90
 
0.3%
116.0376 80
 
0.2%
34.5239 68
 
0.2%
-87.704 67
 
0.2%
34.4593 66
 
0.2%
-151.727 60
 
0.2%
-151.7408 58
 
0.2%
-151.7887 58
 
0.2%
-151.7284 58
 
0.2%
Other values (9640) 33668
97.9%
ValueCountFrequency (%)
-179.8594 2
 
< 0.1%
-179.8481 14
< 0.1%
-179.4949 2
 
< 0.1%
-179.4872 2
 
< 0.1%
-179.3358 2
 
< 0.1%
-178.595 4
 
< 0.1%
-177.35 1
 
< 0.1%
-176.6 4
 
< 0.1%
-176.4667 3
 
< 0.1%
-175.83 1
 
< 0.1%
ValueCountFrequency (%)
179.9645 10
 
< 0.1%
179.9498 4
 
< 0.1%
179.9453 2
 
< 0.1%
179.9444 2
 
< 0.1%
179.9442 16
< 0.1%
179.9438 2
 
< 0.1%
179.933 1
 
< 0.1%
179.9298 4
 
< 0.1%
179.9295 2
 
< 0.1%
179.9013 32
0.1%

Ocean_Name
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size268.8 KiB
Pacific
17347 
Atlantic
13313 
Indian
2322 
Red Sea
 
1042
Arabian Gulf
 
369

Length

Max length12
Median length7
Mean length7.3732155
Min length6

Characters and Unicode

Total characters253587
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAtlantic
2nd rowPacific
3rd rowAtlantic
4th rowAtlantic
5th rowAtlantic

Common Values

ValueCountFrequency (%)
Pacific 17347
50.4%
Atlantic 13313
38.7%
Indian 2322
 
6.8%
Red Sea 1042
 
3.0%
Arabian Gulf 369
 
1.1%

Length

2024-06-27T12:24:26.177741image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-27T12:24:26.218801image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
pacific 17347
48.4%
atlantic 13313
37.2%
indian 2322
 
6.5%
red 1042
 
2.9%
sea 1042
 
2.9%
arabian 369
 
1.0%
gulf 369
 
1.0%

Most occurring characters

ValueCountFrequency (%)
i 50698
20.0%
c 48007
18.9%
a 34762
13.7%
t 26626
10.5%
n 18326
 
7.2%
f 17716
 
7.0%
P 17347
 
6.8%
A 13682
 
5.4%
l 13682
 
5.4%
d 3364
 
1.3%
Other values (9) 9377
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 216372
85.3%
Uppercase Letter 35804
 
14.1%
Space Separator 1411
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 50698
23.4%
c 48007
22.2%
a 34762
16.1%
t 26626
12.3%
n 18326
 
8.5%
f 17716
 
8.2%
l 13682
 
6.3%
d 3364
 
1.6%
e 2084
 
1.0%
r 369
 
0.2%
Other values (2) 738
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
P 17347
48.4%
A 13682
38.2%
I 2322
 
6.5%
R 1042
 
2.9%
S 1042
 
2.9%
G 369
 
1.0%
Space Separator
ValueCountFrequency (%)
1411
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 252176
99.4%
Common 1411
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 50698
20.1%
c 48007
19.0%
a 34762
13.8%
t 26626
10.6%
n 18326
 
7.3%
f 17716
 
7.0%
P 17347
 
6.9%
A 13682
 
5.4%
l 13682
 
5.4%
d 3364
 
1.3%
Other values (8) 7966
 
3.2%
Common
ValueCountFrequency (%)
1411
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 253587
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 50698
20.0%
c 48007
18.9%
a 34762
13.7%
t 26626
10.5%
n 18326
 
7.2%
f 17716
 
7.0%
P 17347
 
6.8%
A 13682
 
5.4%
l 13682
 
5.4%
d 3364
 
1.3%
Other values (9) 9377
 
3.7%

Reef_ID
Text

MISSING 

Distinct4102
Distinct (%)18.3%
Missing11972
Missing (%)34.8%
Memory size268.8 KiB
2024-06-27T12:24:26.287637image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length27
Median length25
Mean length20.503055
Min length5

Characters and Unicode

Total characters459699
Distinct characters62
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)0.1%

Sample

1st row146.52.850E.19.07.313S
2nd row146.52.850E.19.07.313S
3rd row146.57.22.5E.18.57.21.8S
4th row146.57.22.5E.18.57.21.8S
5th row146.57.375E.18.57.364S
ValueCountFrequency (%)
151.47.039w.16.29.965s 90
 
0.4%
116.2.15.5e.8.20.15.5s 80
 
0.4%
34.31.26e.28.33.26.9n 68
 
0.3%
99.50.099e.10.7.366n 68
 
0.3%
34.27.560e.28.26.221n 66
 
0.3%
151.43.621w.16.32.544s 60
 
0.3%
151.43.706w.16.32.117s 58
 
0.3%
151.44.447w.16.32.562s 58
 
0.3%
151.47.324w.16.32.380s 58
 
0.3%
151.43.791w.16.32.087s 56
 
0.2%
Other values (4094) 21771
97.0%
2024-06-27T12:24:26.412118image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 116518
25.3%
1 56917
12.4%
2 36014
 
7.8%
5 32157
 
7.0%
4 31277
 
6.8%
3 29259
 
6.4%
6 23374
 
5.1%
8 21197
 
4.6%
7 20339
 
4.4%
0 19911
 
4.3%
Other values (52) 72736
15.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 288663
62.8%
Other Punctuation 116518
25.3%
Uppercase Letter 46879
 
10.2%
Lowercase Letter 7627
 
1.7%
Space Separator 12
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 16166
34.5%
N 15595
33.3%
S 8073
17.2%
W 5415
 
11.6%
M 240
 
0.5%
T 213
 
0.5%
L 182
 
0.4%
P 118
 
0.3%
I 117
 
0.2%
B 109
 
0.2%
Other values (15) 651
 
1.4%
Lowercase Letter
ValueCountFrequency (%)
a 1081
14.2%
e 730
9.6%
o 715
9.4%
u 648
 
8.5%
t 646
 
8.5%
n 511
 
6.7%
i 462
 
6.1%
s 461
 
6.0%
r 383
 
5.0%
h 338
 
4.4%
Other values (15) 1652
21.7%
Decimal Number
ValueCountFrequency (%)
1 56917
19.7%
2 36014
12.5%
5 32157
11.1%
4 31277
10.8%
3 29259
10.1%
6 23374
8.1%
8 21197
 
7.3%
7 20339
 
7.0%
0 19911
 
6.9%
9 18218
 
6.3%
Other Punctuation
ValueCountFrequency (%)
. 116518
100.0%
Space Separator
ValueCountFrequency (%)
12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 405193
88.1%
Latin 54506
 
11.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 16166
29.7%
N 15595
28.6%
S 8073
14.8%
W 5415
 
9.9%
a 1081
 
2.0%
e 730
 
1.3%
o 715
 
1.3%
u 648
 
1.2%
t 646
 
1.2%
n 511
 
0.9%
Other values (40) 4926
 
9.0%
Common
ValueCountFrequency (%)
. 116518
28.8%
1 56917
14.0%
2 36014
 
8.9%
5 32157
 
7.9%
4 31277
 
7.7%
3 29259
 
7.2%
6 23374
 
5.8%
8 21197
 
5.2%
7 20339
 
5.0%
0 19911
 
4.9%
Other values (2) 18230
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 459699
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 116518
25.3%
1 56917
12.4%
2 36014
 
7.8%
5 32157
 
7.0%
4 31277
 
6.8%
3 29259
 
6.4%
6 23374
 
5.1%
8 21197
 
4.6%
7 20339
 
4.4%
0 19911
 
4.3%
Other values (52) 72736
15.8%

Realm_Name
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size268.8 KiB
Central Indo-Pacific
14779 
Tropical Atlantic
13299 
Western Indo-Pacific
3486 
Eastern Indo-Pacific
1677 
Temperate Australasia
 
536
Other values (3)
 
616

Length

Max length27
Median length20
Mean length18.957957
Min length17

Characters and Unicode

Total characters652021
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTropical Atlantic
2nd rowEastern Indo-Pacific
3rd rowTropical Atlantic
4th rowTropical Atlantic
5th rowTropical Atlantic

Common Values

ValueCountFrequency (%)
Central Indo-Pacific 14779
43.0%
Tropical Atlantic 13299
38.7%
Western Indo-Pacific 3486
 
10.1%
Eastern Indo-Pacific 1677
 
4.9%
Temperate Australasia 536
 
1.6%
Temperate Northern Pacific 508
 
1.5%
Tropical Eastern Pacific 94
 
0.3%
Temperate Northern Atlantic 14
 
< 0.1%

Length

2024-06-27T12:24:26.466563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-27T12:24:26.509387image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
indo-pacific 19942
28.7%
central 14779
21.3%
tropical 13393
19.3%
atlantic 13313
19.2%
western 3486
 
5.0%
eastern 1771
 
2.6%
temperate 1058
 
1.5%
pacific 602
 
0.9%
australasia 536
 
0.8%
northern 522
 
0.8%

Most occurring characters

ValueCountFrequency (%)
i 68330
 
10.5%
c 67794
 
10.4%
a 66466
 
10.2%
n 53813
 
8.3%
t 48778
 
7.5%
l 42021
 
6.4%
r 36067
 
5.5%
35009
 
5.4%
o 33857
 
5.2%
e 27218
 
4.2%
Other values (16) 172668
26.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 507726
77.9%
Uppercase Letter 89344
 
13.7%
Space Separator 35009
 
5.4%
Dash Punctuation 19942
 
3.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 68330
13.5%
c 67794
13.4%
a 66466
13.1%
n 53813
10.6%
t 48778
9.6%
l 42021
8.3%
r 36067
7.1%
o 33857
6.7%
e 27218
 
5.4%
f 20544
 
4.0%
Other values (6) 42838
8.4%
Uppercase Letter
ValueCountFrequency (%)
P 20544
23.0%
I 19942
22.3%
C 14779
16.5%
T 14451
16.2%
A 13849
15.5%
W 3486
 
3.9%
E 1771
 
2.0%
N 522
 
0.6%
Space Separator
ValueCountFrequency (%)
35009
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19942
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 597070
91.6%
Common 54951
 
8.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 68330
11.4%
c 67794
11.4%
a 66466
11.1%
n 53813
9.0%
t 48778
 
8.2%
l 42021
 
7.0%
r 36067
 
6.0%
o 33857
 
5.7%
e 27218
 
4.6%
f 20544
 
3.4%
Other values (14) 132182
22.1%
Common
ValueCountFrequency (%)
35009
63.7%
- 19942
36.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 652021
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 68330
 
10.5%
c 67794
 
10.4%
a 66466
 
10.2%
n 53813
 
8.3%
t 48778
 
7.5%
l 42021
 
6.4%
r 36067
 
5.5%
35009
 
5.4%
o 33857
 
5.2%
e 27218
 
4.2%
Other values (16) 172668
26.5%
Distinct113
Distinct (%)0.3%
Missing3
Missing (%)< 0.1%
Memory size268.8 KiB
2024-06-27T12:24:26.598969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length52
Median length42
Mean length23.412504
Min length4

Characters and Unicode

Total characters805156
Distinct characters50
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st rowCuba and Cayman Islands
2nd rowSociety Islands French Polynesia
3rd rowHispaniola Puerto Rico and Lesser Antilles
4th rowHispaniola Puerto Rico and Lesser Antilles
5th rowHispaniola Puerto Rico and Lesser Antilles
ValueCountFrequency (%)
and 16973
 
13.6%
islands 5290
 
4.2%
sea 5090
 
4.1%
caribbean 4352
 
3.5%
bahamas 3988
 
3.2%
keys 3988
 
3.2%
florida 3988
 
3.2%
west 3822
 
3.1%
south-east 3770
 
3.0%
belize 3749
 
3.0%
Other values (172) 70193
56.1%
2024-06-27T12:24:26.742898image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 102578
12.7%
90813
 
11.3%
e 70698
 
8.8%
n 58317
 
7.2%
s 52492
 
6.5%
i 45068
 
5.6%
l 38610
 
4.8%
r 35986
 
4.5%
d 34235
 
4.3%
t 33687
 
4.2%
Other values (40) 242672
30.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 614502
76.3%
Uppercase Letter 95931
 
11.9%
Space Separator 90813
 
11.3%
Dash Punctuation 3910
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 102578
16.7%
e 70698
11.5%
n 58317
9.5%
s 52492
8.5%
i 45068
7.3%
l 38610
 
6.3%
r 35986
 
5.9%
d 34235
 
5.6%
t 33687
 
5.5%
o 32009
 
5.2%
Other values (16) 110822
18.0%
Uppercase Letter
ValueCountFrequency (%)
S 17779
18.5%
B 11361
11.8%
C 9045
9.4%
A 7022
 
7.3%
R 6402
 
6.7%
F 6335
 
6.6%
P 5967
 
6.2%
I 5598
 
5.8%
K 5204
 
5.4%
H 3573
 
3.7%
Other values (12) 17645
18.4%
Space Separator
ValueCountFrequency (%)
90813
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3910
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 710433
88.2%
Common 94723
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 102578
14.4%
e 70698
 
10.0%
n 58317
 
8.2%
s 52492
 
7.4%
i 45068
 
6.3%
l 38610
 
5.4%
r 35986
 
5.1%
d 34235
 
4.8%
t 33687
 
4.7%
o 32009
 
4.5%
Other values (38) 206753
29.1%
Common
ValueCountFrequency (%)
90813
95.9%
- 3910
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 805156
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 102578
12.7%
90813
 
11.3%
e 70698
 
8.8%
n 58317
 
7.2%
s 52492
 
6.5%
i 45068
 
5.6%
l 38610
 
4.8%
r 35986
 
4.5%
d 34235
 
4.3%
t 33687
 
4.2%
Other values (40) 242672
30.1%
Distinct89
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size268.8 KiB
2024-06-27T12:24:26.837044image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length44
Median length21
Mean length9.2324019
Min length4

Characters and Unicode

Total characters317530
Distinct characters50
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowCuba
2nd rowFrench Polynesia
3rd rowUnited Kingdom
4th rowUnited States
5th rowUnited States
ValueCountFrequency (%)
united 4478
 
10.1%
states 4369
 
9.8%
malaysia 4240
 
9.5%
australia 2622
 
5.9%
mexico 2068
 
4.6%
indonesia 1745
 
3.9%
philippines 1731
 
3.9%
french 1360
 
3.1%
polynesia 1360
 
3.1%
jamaica 1077
 
2.4%
Other values (107) 19461
43.7%
2024-06-27T12:24:26.975421image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 48491
15.3%
i 34591
 
10.9%
e 25431
 
8.0%
n 23484
 
7.4%
s 20979
 
6.6%
t 19815
 
6.2%
l 15891
 
5.0%
d 11580
 
3.6%
10118
 
3.2%
o 9880
 
3.1%
Other values (40) 97270
30.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 263701
83.0%
Uppercase Letter 43675
 
13.8%
Space Separator 10118
 
3.2%
Other Punctuation 36
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 48491
18.4%
i 34591
13.1%
e 25431
9.6%
n 23484
8.9%
s 20979
8.0%
t 19815
7.5%
l 15891
 
6.0%
d 11580
 
4.4%
o 9880
 
3.7%
r 7872
 
3.0%
Other values (16) 45687
17.3%
Uppercase Letter
ValueCountFrequency (%)
M 7485
17.1%
S 5157
11.8%
U 4478
10.3%
P 3619
8.3%
A 3321
7.6%
F 3185
7.3%
C 2562
 
5.9%
I 2341
 
5.4%
J 2075
 
4.8%
B 2004
 
4.6%
Other values (12) 7448
17.1%
Space Separator
ValueCountFrequency (%)
10118
100.0%
Other Punctuation
ValueCountFrequency (%)
& 36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 307376
96.8%
Common 10154
 
3.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 48491
15.8%
i 34591
 
11.3%
e 25431
 
8.3%
n 23484
 
7.6%
s 20979
 
6.8%
t 19815
 
6.4%
l 15891
 
5.2%
d 11580
 
3.8%
o 9880
 
3.2%
r 7872
 
2.6%
Other values (38) 89362
29.1%
Common
ValueCountFrequency (%)
10118
99.6%
& 36
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 317530
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 48491
15.3%
i 34591
 
10.9%
e 25431
 
8.0%
n 23484
 
7.4%
s 20979
 
6.6%
t 19815
 
6.2%
l 15891
 
5.0%
d 11580
 
3.6%
10118
 
3.2%
o 9880
 
3.1%
Other values (40) 97270
30.6%
Distinct441
Distinct (%)1.3%
Missing68
Missing (%)0.2%
Memory size268.8 KiB
2024-06-27T12:24:27.089701image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length43
Median length33
Mean length11.785171
Min length3

Characters and Unicode

Total characters404526
Distinct characters57
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique59 ?
Unique (%)0.2%

Sample

1st rowHavana
2nd rowSociety Islands
3rd rowBritish Virgin Islands
4th rowUS Virgin Islands
5th rowUS Virgin Islands
ValueCountFrequency (%)
islands 3521
 
5.8%
florida 3197
 
5.3%
queensland 2441
 
4.0%
roo 2050
 
3.4%
quintana 2050
 
3.4%
sabah 2007
 
3.3%
province 1598
 
2.6%
society 1261
 
2.1%
parish 1242
 
2.1%
governorate 1241
 
2.1%
Other values (515) 39787
65.9%
2024-06-27T12:24:27.254604image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 51386
 
12.7%
n 32933
 
8.1%
e 28355
 
7.0%
o 26301
 
6.5%
26070
 
6.4%
i 25716
 
6.4%
r 23195
 
5.7%
s 19695
 
4.9%
l 17529
 
4.3%
t 16503
 
4.1%
Other values (47) 136843
33.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 316082
78.1%
Uppercase Letter 61307
 
15.2%
Space Separator 26070
 
6.4%
Other Punctuation 648
 
0.2%
Dash Punctuation 289
 
0.1%
Open Punctuation 65
 
< 0.1%
Close Punctuation 65
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 51386
16.3%
n 32933
10.4%
e 28355
9.0%
o 26301
8.3%
i 25716
8.1%
r 23195
 
7.3%
s 19695
 
6.2%
l 17529
 
5.5%
t 16503
 
5.2%
u 15390
 
4.9%
Other values (16) 59079
18.7%
Uppercase Letter
ValueCountFrequency (%)
S 8975
14.6%
P 4731
 
7.7%
Q 4555
 
7.4%
R 4161
 
6.8%
I 4117
 
6.7%
C 3571
 
5.8%
F 3385
 
5.5%
B 3287
 
5.4%
T 3277
 
5.3%
A 3113
 
5.1%
Other values (15) 18135
29.6%
Other Punctuation
ValueCountFrequency (%)
. 587
90.6%
' 61
 
9.4%
Space Separator
ValueCountFrequency (%)
26070
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 289
100.0%
Open Punctuation
ValueCountFrequency (%)
( 65
100.0%
Close Punctuation
ValueCountFrequency (%)
) 65
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 377389
93.3%
Common 27137
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 51386
13.6%
n 32933
 
8.7%
e 28355
 
7.5%
o 26301
 
7.0%
i 25716
 
6.8%
r 23195
 
6.1%
s 19695
 
5.2%
l 17529
 
4.6%
t 16503
 
4.4%
u 15390
 
4.1%
Other values (41) 120386
31.9%
Common
ValueCountFrequency (%)
26070
96.1%
. 587
 
2.2%
- 289
 
1.1%
( 65
 
0.2%
) 65
 
0.2%
' 61
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 404526
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 51386
 
12.7%
n 32933
 
8.1%
e 28355
 
7.0%
o 26301
 
6.5%
26070
 
6.4%
i 25716
 
6.4%
r 23195
 
5.7%
s 19695
 
4.9%
l 17529
 
4.3%
t 16503
 
4.1%
Other values (47) 136843
33.8%

City_Town_Name
Text

MISSING 

Distinct1703
Distinct (%)5.1%
Missing970
Missing (%)2.8%
Memory size268.8 KiB
2024-06-27T12:24:27.364355image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length55
Median length34
Mean length12.4683
Min length2

Characters and Unicode

Total characters416728
Distinct characters66
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique500 ?
Unique (%)1.5%

Sample

1st rowHavana
2nd rowMoorea
3rd rowPeter Island
4th rowSt. Croix
5th rowSt. Croix
ValueCountFrequency (%)
island 3201
 
4.8%
county 3197
 
4.8%
bora 2128
 
3.2%
monroe 1963
 
2.9%
islands 1773
 
2.7%
regency 1550
 
2.3%
district 1331
 
2.0%
reef 1315
 
2.0%
park 1085
 
1.6%
othon 1080
 
1.6%
Other values (1872) 48240
72.1%
2024-06-27T12:24:27.519015image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 49812
 
12.0%
33440
 
8.0%
n 31169
 
7.5%
o 29312
 
7.0%
e 27349
 
6.6%
r 23877
 
5.7%
i 18930
 
4.5%
l 18208
 
4.4%
t 18011
 
4.3%
s 16204
 
3.9%
Other values (56) 150416
36.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 312563
75.0%
Uppercase Letter 67394
 
16.2%
Space Separator 33440
 
8.0%
Other Punctuation 1892
 
0.5%
Dash Punctuation 1388
 
0.3%
Decimal Number 51
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 49812
15.9%
n 31169
10.0%
o 29312
9.4%
e 27349
8.7%
r 23877
 
7.6%
i 18930
 
6.1%
l 18208
 
5.8%
t 18011
 
5.8%
s 16204
 
5.2%
u 15837
 
5.1%
Other values (17) 63854
20.4%
Uppercase Letter
ValueCountFrequency (%)
C 8402
12.5%
B 7428
11.0%
M 7068
10.5%
P 5324
 
7.9%
I 5318
 
7.9%
S 4104
 
6.1%
R 3850
 
5.7%
D 3359
 
5.0%
K 3094
 
4.6%
A 2993
 
4.4%
Other values (16) 16454
24.4%
Decimal Number
ValueCountFrequency (%)
1 15
29.4%
0 14
27.5%
3 10
19.6%
5 9
17.6%
7 1
 
2.0%
8 1
 
2.0%
9 1
 
2.0%
Other Punctuation
ValueCountFrequency (%)
. 1404
74.2%
' 441
 
23.3%
/ 45
 
2.4%
& 2
 
0.1%
Space Separator
ValueCountFrequency (%)
33440
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1388
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 379957
91.2%
Common 36771
 
8.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 49812
 
13.1%
n 31169
 
8.2%
o 29312
 
7.7%
e 27349
 
7.2%
r 23877
 
6.3%
i 18930
 
5.0%
l 18208
 
4.8%
t 18011
 
4.7%
s 16204
 
4.3%
u 15837
 
4.2%
Other values (43) 131248
34.5%
Common
ValueCountFrequency (%)
33440
90.9%
. 1404
 
3.8%
- 1388
 
3.8%
' 441
 
1.2%
/ 45
 
0.1%
1 15
 
< 0.1%
0 14
 
< 0.1%
3 10
 
< 0.1%
5 9
 
< 0.1%
& 2
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 416727
> 99.9%
Latin Ext Additional 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 49812
 
12.0%
33440
 
8.0%
n 31169
 
7.5%
o 29312
 
7.0%
e 27349
 
6.6%
r 23877
 
5.7%
i 18930
 
4.5%
l 18208
 
4.4%
t 18011
 
4.3%
s 16204
 
3.9%
Other values (55) 150415
36.1%
Latin Ext Additional
ValueCountFrequency (%)
ầ 1
100.0%

Distance_to_Shore
Real number (ℝ)

Distinct10538
Distinct (%)30.6%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3678.3364
Minimum3.2
Maximum299218.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size268.8 KiB
2024-06-27T12:24:27.579827image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3.2
5-th percentile36.61
Q1126.66
median478.09
Q31844.54
95-th percentile13270.8
Maximum299218.5
Range299215.3
Interquartile range (IQR)1717.88

Descriptive statistics

Standard deviation13402.698
Coefficient of variation (CV)3.6436848
Kurtosis114.80516
Mean3678.3364
Median Absolute Deviation (MAD)412.87
Skewness9.3048284
Sum1.2650167 × 108
Variance1.7963232 × 108
MonotonicityNot monotonic
2024-06-27T12:24:27.622977image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
953.11 90
 
0.3%
158.91 80
 
0.2%
79.08 72
 
0.2%
31.89 68
 
0.2%
182.74 68
 
0.2%
116.09 60
 
0.2%
365.35 58
 
0.2%
2350.5 58
 
0.2%
357.92 58
 
0.2%
882.08 56
 
0.2%
Other values (10528) 33723
98.1%
ValueCountFrequency (%)
3.2 1
 
< 0.1%
3.68 2
< 0.1%
3.74 2
< 0.1%
3.79 2
< 0.1%
3.83 2
< 0.1%
3.85 1
 
< 0.1%
4.01 4
< 0.1%
4.45 1
 
< 0.1%
4.59 3
< 0.1%
4.61 4
< 0.1%
ValueCountFrequency (%)
299218.5 1
< 0.1%
281663.76 1
< 0.1%
238898.44 1
< 0.1%
235442.75 1
< 0.1%
233970.5 1
< 0.1%
233763.64 1
< 0.1%
230967.11 1
< 0.1%
230192.64 1
< 0.1%
228892.5 1
< 0.1%
226662.83 1
< 0.1%

Exposure
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size268.8 KiB
Sheltered
19394 
Exposed
12225 
Sometimes
2774 

Length

Max length9
Median length9
Mean length8.2890995
Min length7

Characters and Unicode

Total characters285087
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowExposed
2nd rowExposed
3rd rowExposed
4th rowExposed
5th rowExposed

Common Values

ValueCountFrequency (%)
Sheltered 19394
56.4%
Exposed 12225
35.5%
Sometimes 2774
 
8.1%

Length

2024-06-27T12:24:27.665419image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-27T12:24:27.705768image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
sheltered 19394
56.4%
exposed 12225
35.5%
sometimes 2774
 
8.1%

Most occurring characters

ValueCountFrequency (%)
e 75955
26.6%
d 31619
11.1%
S 22168
 
7.8%
t 22168
 
7.8%
h 19394
 
6.8%
l 19394
 
6.8%
r 19394
 
6.8%
o 14999
 
5.3%
s 14999
 
5.3%
E 12225
 
4.3%
Other values (4) 32772
11.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 250694
87.9%
Uppercase Letter 34393
 
12.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 75955
30.3%
d 31619
12.6%
t 22168
 
8.8%
h 19394
 
7.7%
l 19394
 
7.7%
r 19394
 
7.7%
o 14999
 
6.0%
s 14999
 
6.0%
x 12225
 
4.9%
p 12225
 
4.9%
Other values (2) 8322
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
S 22168
64.5%
E 12225
35.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 285087
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 75955
26.6%
d 31619
11.1%
S 22168
 
7.8%
t 22168
 
7.8%
h 19394
 
6.8%
l 19394
 
6.8%
r 19394
 
6.8%
o 14999
 
5.3%
s 14999
 
5.3%
E 12225
 
4.3%
Other values (4) 32772
11.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 285087
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 75955
26.6%
d 31619
11.1%
S 22168
 
7.8%
t 22168
 
7.8%
h 19394
 
6.8%
l 19394
 
6.8%
r 19394
 
6.8%
o 14999
 
5.3%
s 14999
 
5.3%
E 12225
 
4.3%
Other values (4) 32772
11.5%

Turbidity
Real number (ℝ)

Distinct1470
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.075649958
Minimum0.0176
Maximum1.2845
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size268.8 KiB
2024-06-27T12:24:27.743963image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0176
5-th percentile0.0271
Q10.0396
median0.057
Q30.0843
95-th percentile0.1942
Maximum1.2845
Range1.2669
Interquartile range (IQR)0.0447

Descriptive statistics

Standard deviation0.061738099
Coefficient of variation (CV)0.81610223
Kurtosis35.497554
Mean0.075649958
Median Absolute Deviation (MAD)0.0207
Skewness3.8660842
Sum2601.829
Variance0.0038115929
MonotonicityNot monotonic
2024-06-27T12:24:27.790732image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0261 335
 
1.0%
0.0279 298
 
0.9%
0.0353 286
 
0.8%
0.0273 277
 
0.8%
0.0286 275
 
0.8%
0.0395 262
 
0.8%
0.0442 242
 
0.7%
0.0513 230
 
0.7%
0.028 230
 
0.7%
0.0278 205
 
0.6%
Other values (1460) 31753
92.3%
ValueCountFrequency (%)
0.0176 3
 
< 0.1%
0.0196 1
 
< 0.1%
0.0202 4
 
< 0.1%
0.0204 5
 
< 0.1%
0.0205 1
 
< 0.1%
0.0208 6
 
< 0.1%
0.0209 1
 
< 0.1%
0.0214 31
0.1%
0.0215 1
 
< 0.1%
0.0216 5
 
< 0.1%
ValueCountFrequency (%)
1.2845 1
 
< 0.1%
1.1635 8
< 0.1%
0.9785 1
 
< 0.1%
0.8039 1
 
< 0.1%
0.7188 3
 
< 0.1%
0.6108 1
 
< 0.1%
0.5804 1
 
< 0.1%
0.5246 1
 
< 0.1%
0.5181 1
 
< 0.1%
0.5143 1
 
< 0.1%

Cyclone_Frequency
Real number (ℝ)

Distinct1623
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.337829
Minimum18.31
Maximum105.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size268.8 KiB
2024-06-27T12:24:27.837569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum18.31
5-th percentile41.4
Q147.94
median51.41
Q356.14
95-th percentile65.38
Maximum105.8
Range87.49
Interquartile range (IQR)8.2

Descriptive statistics

Standard deviation7.6058057
Coefficient of variation (CV)0.14532138
Kurtosis3.7176622
Mean52.337829
Median Absolute Deviation (MAD)3.73
Skewness0.86488041
Sum1800055
Variance57.84828
MonotonicityNot monotonic
2024-06-27T12:24:27.880277image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49.1 720
 
2.1%
52.33 485
 
1.4%
47.94 419
 
1.2%
49.45 369
 
1.1%
54.44 327
 
1.0%
47.39 311
 
0.9%
49.54 280
 
0.8%
47.68 277
 
0.8%
56.63 271
 
0.8%
49.62 256
 
0.7%
Other values (1613) 30678
89.2%
ValueCountFrequency (%)
18.31 1
 
< 0.1%
19.86 23
0.1%
21.71 3
 
< 0.1%
21.8 1
 
< 0.1%
23.07 1
 
< 0.1%
25.13 1
 
< 0.1%
25.23 4
 
< 0.1%
25.3 2
 
< 0.1%
25.9 3
 
< 0.1%
26.03 5
 
< 0.1%
ValueCountFrequency (%)
105.8 1
 
< 0.1%
102.37 1
 
< 0.1%
99.43 5
 
< 0.1%
99.22 2
 
< 0.1%
94.95 7
 
< 0.1%
92.3 52
0.2%
91.11 6
 
< 0.1%
88.72 11
 
< 0.1%
88.08 54
0.2%
87.59 1
 
< 0.1%

Date_Day
Real number (ℝ)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.87576
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size268.8 KiB
2024-06-27T12:24:27.920424image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q110
median15
Q322
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.7612597
Coefficient of variation (CV)0.48887485
Kurtosis-0.75189437
Mean15.87576
Median Absolute Deviation (MAD)6
Skewness0.050980135
Sum546015
Variance60.237152
MonotonicityNot monotonic
2024-06-27T12:24:27.957697image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
15 6820
 
19.8%
10 1091
 
3.2%
9 1044
 
3.0%
13 1044
 
3.0%
21 1034
 
3.0%
20 1034
 
3.0%
19 1022
 
3.0%
14 1015
 
3.0%
12 1013
 
2.9%
23 997
 
2.9%
Other values (21) 18279
53.1%
ValueCountFrequency (%)
1 807
2.3%
2 764
2.2%
3 693
2.0%
4 732
2.1%
5 785
2.3%
6 907
2.6%
7 976
2.8%
8 962
2.8%
9 1044
3.0%
10 1091
3.2%
ValueCountFrequency (%)
31 479
1.4%
30 851
2.5%
29 915
2.7%
28 890
2.6%
27 960
2.8%
26 896
2.6%
25 968
2.8%
24 918
2.7%
23 997
2.9%
22 982
2.9%

Date_Month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9198965
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size268.8 KiB
2024-06-27T12:24:27.994578image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median7
Q39
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9087179
Coefficient of variation (CV)0.42034124
Kurtosis-0.94141907
Mean6.9198965
Median Absolute Deviation (MAD)2
Skewness-0.17290549
Sum237996
Variance8.4606395
MonotonicityNot monotonic
2024-06-27T12:24:28.025268image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
9 4740
13.8%
8 4684
13.6%
5 3535
10.3%
10 3155
9.2%
6 3068
8.9%
3 3009
8.7%
7 2974
8.6%
4 2828
8.2%
11 2450
7.1%
12 1517
 
4.4%
Other values (2) 2433
7.1%
ValueCountFrequency (%)
1 946
 
2.8%
2 1487
 
4.3%
3 3009
8.7%
4 2828
8.2%
5 3535
10.3%
6 3068
8.9%
7 2974
8.6%
8 4684
13.6%
9 4740
13.8%
10 3155
9.2%
ValueCountFrequency (%)
12 1517
 
4.4%
11 2450
7.1%
10 3155
9.2%
9 4740
13.8%
8 4684
13.6%
7 2974
8.6%
6 3068
8.9%
5 3535
10.3%
4 2828
8.2%
3 3009
8.7%

Date_Year
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2008.8082
Minimum1983
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size268.8 KiB
2024-06-27T12:24:28.061730image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1983
5-th percentile1999
Q12005
median2008
Q32014
95-th percentile2018
Maximum2019
Range36
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.6903919
Coefficient of variation (CV)0.0028327204
Kurtosis-0.61552488
Mean2008.8082
Median Absolute Deviation (MAD)4
Skewness-0.0088067239
Sum69088939
Variance32.38056
MonotonicityNot monotonic
2024-06-27T12:24:28.100881image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
2005 3983
 
11.6%
2006 2792
 
8.1%
2009 1956
 
5.7%
2008 1918
 
5.6%
2016 1811
 
5.3%
2004 1710
 
5.0%
2003 1664
 
4.8%
2010 1632
 
4.7%
2017 1583
 
4.6%
2007 1561
 
4.5%
Other values (24) 13783
40.1%
ValueCountFrequency (%)
1983 15
< 0.1%
1986 3
 
< 0.1%
1987 17
< 0.1%
1988 6
 
< 0.1%
1990 3
 
< 0.1%
1991 7
 
< 0.1%
1992 8
< 0.1%
1993 9
< 0.1%
1994 19
0.1%
1995 6
 
< 0.1%
ValueCountFrequency (%)
2019 1174
3.4%
2018 1243
3.6%
2017 1583
4.6%
2016 1811
5.3%
2015 1559
4.5%
2014 1409
4.1%
2013 1521
4.4%
2012 1402
4.1%
2011 1357
3.9%
2010 1632
4.7%

Depth_m
Real number (ℝ)

MISSING 

Distinct461
Distinct (%)1.4%
Missing1677
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean7.0575844
Minimum0
Maximum50.3
Zeros16
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size268.8 KiB
2024-06-27T12:24:28.146076image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.5
Q14
median6
Q310
95-th percentile14.7
Maximum50.3
Range50.3
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.2357235
Coefficient of variation (CV)0.60016619
Kurtosis4.3875008
Mean7.0575844
Median Absolute Deviation (MAD)3
Skewness1.2725168
Sum230895.93
Variance17.941354
MonotonicityNot monotonic
2024-06-27T12:24:28.190701image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 3510
 
10.2%
10 3470
 
10.1%
3 2172
 
6.3%
6 2120
 
6.2%
4 1388
 
4.0%
8 1148
 
3.3%
12 1135
 
3.3%
7 1106
 
3.2%
2 1056
 
3.1%
1 860
 
2.5%
Other values (451) 14751
42.9%
(Missing) 1677
 
4.9%
ValueCountFrequency (%)
0 16
 
< 0.1%
0.1 13
 
< 0.1%
0.2 5
 
< 0.1%
0.3 19
 
0.1%
0.4 15
 
< 0.1%
0.5 71
0.2%
0.52 1
 
< 0.1%
0.6 38
0.1%
0.65 2
 
< 0.1%
0.7 48
0.1%
ValueCountFrequency (%)
50.3 1
 
< 0.1%
50 1
 
< 0.1%
42.7 1
 
< 0.1%
42 1
 
< 0.1%
40 4
 
< 0.1%
39.6 1
 
< 0.1%
39 11
< 0.1%
36 7
< 0.1%
33 8
< 0.1%
30.3 1
 
< 0.1%

Substrate_Name
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing12035
Missing (%)35.0%
Memory size268.8 KiB
Hard Coral
11179 
Nutrient Indicator Algae
10961 
Fleshy Seaweed
 
218

Length

Max length24
Median length19
Mean length16.902496
Min length10

Characters and Unicode

Total characters377906
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHard Coral
2nd rowNutrient Indicator Algae
3rd rowHard Coral
4th rowNutrient Indicator Algae
5th rowHard Coral

Common Values

ValueCountFrequency (%)
Hard Coral 11179
32.5%
Nutrient Indicator Algae 10961
31.9%
Fleshy Seaweed 218
 
0.6%
(Missing) 12035
35.0%

Length

2024-06-27T12:24:28.231494image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-27T12:24:28.272552image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
hard 11179
20.1%
coral 11179
20.1%
nutrient 10961
19.7%
indicator 10961
19.7%
algae 10961
19.7%
fleshy 218
 
0.4%
seaweed 218
 
0.4%

Most occurring characters

ValueCountFrequency (%)
a 44498
11.8%
r 44280
11.7%
33319
 
8.8%
t 32883
 
8.7%
e 22794
 
6.0%
d 22358
 
5.9%
l 22358
 
5.9%
o 22140
 
5.9%
n 21922
 
5.8%
i 21922
 
5.8%
Other values (14) 89432
23.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 288910
76.5%
Uppercase Letter 55677
 
14.7%
Space Separator 33319
 
8.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 44498
15.4%
r 44280
15.3%
t 32883
11.4%
e 22794
7.9%
d 22358
7.7%
l 22358
7.7%
o 22140
7.7%
n 21922
7.6%
i 21922
7.6%
u 10961
 
3.8%
Other values (6) 22794
7.9%
Uppercase Letter
ValueCountFrequency (%)
H 11179
20.1%
C 11179
20.1%
N 10961
19.7%
I 10961
19.7%
A 10961
19.7%
F 218
 
0.4%
S 218
 
0.4%
Space Separator
ValueCountFrequency (%)
33319
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 344587
91.2%
Common 33319
 
8.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 44498
12.9%
r 44280
12.9%
t 32883
9.5%
e 22794
 
6.6%
d 22358
 
6.5%
l 22358
 
6.5%
o 22140
 
6.4%
n 21922
 
6.4%
i 21922
 
6.4%
H 11179
 
3.2%
Other values (13) 78253
22.7%
Common
ValueCountFrequency (%)
33319
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 377906
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 44498
11.8%
r 44280
11.7%
33319
 
8.8%
t 32883
 
8.7%
e 22794
 
6.0%
d 22358
 
5.9%
l 22358
 
5.9%
o 22140
 
5.9%
n 21922
 
5.8%
i 21922
 
5.8%
Other values (14) 89432
23.7%

Percent_Bleaching
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2239
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.6322778
Minimum0
Maximum100
Zeros16553
Zeros (%)48.1%
Negative0
Negative (%)0.0%
Memory size268.8 KiB
2024-06-27T12:24:28.313885image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.25
Q36.06
95-th percentile69.212
Maximum100
Range100
Interquartile range (IQR)6.06

Descriptive statistics

Standard deviation20.205675
Coefficient of variation (CV)2.0977047
Kurtosis6.0030798
Mean9.6322778
Median Absolute Deviation (MAD)0.25
Skewness2.587351
Sum331282.93
Variance408.26929
MonotonicityNot monotonic
2024-06-27T12:24:28.357844image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16553
48.1%
5.5 1180
 
3.4%
75 912
 
2.7%
0.25 882
 
2.6%
1 703
 
2.0%
0.5 678
 
2.0%
30.5 655
 
1.9%
5 483
 
1.4%
1.25 467
 
1.4%
0.75 397
 
1.2%
Other values (2229) 11483
33.4%
ValueCountFrequency (%)
0 16553
48.1%
0.01 26
 
0.1%
0.02 49
 
0.1%
0.03 2
 
< 0.1%
0.04 4
 
< 0.1%
0.05 16
 
< 0.1%
0.06 2
 
< 0.1%
0.08 6
 
< 0.1%
0.1 40
 
0.1%
0.11 2
 
< 0.1%
ValueCountFrequency (%)
100 120
0.3%
99 5
 
< 0.1%
98.8 1
 
< 0.1%
98.5 2
 
< 0.1%
98.4 1
 
< 0.1%
98.3 1
 
< 0.1%
98 5
 
< 0.1%
97.5 2
 
< 0.1%
97.22 1
 
< 0.1%
97.02 1
 
< 0.1%

Windspeed
Real number (ℝ)

Distinct18
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7574733
Minimum0
Maximum15
Zeros26
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size268.8 KiB
2024-06-27T12:24:28.395059image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median5
Q36
95-th percentile8
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.069204
Coefficient of variation (CV)0.43493759
Kurtosis0.13167083
Mean4.7574733
Median Absolute Deviation (MAD)1
Skewness0.40867645
Sum163623.78
Variance4.281605
MonotonicityNot monotonic
2024-06-27T12:24:28.428299image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
4 6290
18.3%
5 6199
18.0%
6 5128
14.9%
3 4935
14.3%
2 3672
10.7%
7 3445
10.0%
8 1981
 
5.8%
1 1342
 
3.9%
9 854
 
2.5%
10 313
 
0.9%
Other values (8) 234
 
0.7%
ValueCountFrequency (%)
0 26
 
0.1%
1 1342
 
3.9%
2 3672
10.7%
2.89 3
 
< 0.1%
3 4935
14.3%
3.11 1
 
< 0.1%
4 6290
18.3%
5 6199
18.0%
6 5128
14.9%
7 3445
10.0%
ValueCountFrequency (%)
15 6
 
< 0.1%
14 15
 
< 0.1%
13 26
 
0.1%
12 54
 
0.2%
11 103
 
0.3%
10 313
 
0.9%
9 854
 
2.5%
8 1981
 
5.8%
7 3445
10.0%
6 5128
14.9%

SSTA
Real number (ℝ)

HIGH CORRELATION 

Distinct614
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27975693
Minimum-4.26
Maximum5.9
Zeros339
Zeros (%)1.0%
Negative12056
Negative (%)35.1%
Memory size268.8 KiB
2024-06-27T12:24:28.471678image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-4.26
5-th percentile-1.08
Q1-0.22
median0.27
Q30.78
95-th percentile1.6
Maximum5.9
Range10.16
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.83138111
Coefficient of variation (CV)2.9717981
Kurtosis2.2250301
Mean0.27975693
Median Absolute Deviation (MAD)0.49
Skewness0.23245434
Sum9621.68
Variance0.69119454
MonotonicityNot monotonic
2024-06-27T12:24:28.516342image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 339
 
1.0%
0.51 263
 
0.8%
0.42 249
 
0.7%
0.46 249
 
0.7%
0.36 243
 
0.7%
0.38 233
 
0.7%
0.15 232
 
0.7%
0.3 232
 
0.7%
0.01 227
 
0.7%
0.16 224
 
0.7%
Other values (604) 31902
92.8%
ValueCountFrequency (%)
-4.26 2
< 0.1%
-4.05 1
< 0.1%
-4 1
< 0.1%
-3.88 1
< 0.1%
-3.86 2
< 0.1%
-3.78 1
< 0.1%
-3.7 1
< 0.1%
-3.65 2
< 0.1%
-3.64 1
< 0.1%
-3.62 1
< 0.1%
ValueCountFrequency (%)
5.9 1
 
< 0.1%
5.48 1
 
< 0.1%
5.4 1
 
< 0.1%
5.14 1
 
< 0.1%
5.1 3
< 0.1%
5.05 3
< 0.1%
4.92 2
< 0.1%
4.84 1
 
< 0.1%
4.83 1
 
< 0.1%
4.82 2
< 0.1%

SSTA_Frequency
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct386
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6623822
Minimum0
Maximum52
Zeros1651
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size268.8 KiB
2024-06-27T12:24:28.559677image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.33
Q13
median6
Q311
95-th percentile19.25
Maximum52
Range52
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.1127268
Coefficient of variation (CV)0.79775802
Kurtosis2.5297892
Mean7.6623822
Median Absolute Deviation (MAD)4
Skewness1.2763879
Sum263532.31
Variance37.365429
MonotonicityNot monotonic
2024-06-27T12:24:28.604132image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 2637
 
7.7%
3 2543
 
7.4%
5 2394
 
7.0%
2 2386
 
6.9%
1 2204
 
6.4%
6 1839
 
5.3%
7 1798
 
5.2%
8 1664
 
4.8%
0 1651
 
4.8%
9 1537
 
4.5%
Other values (376) 13740
39.9%
ValueCountFrequency (%)
0 1651
4.8%
0.11 1
 
< 0.1%
0.17 8
 
< 0.1%
0.2 2
 
< 0.1%
0.25 31
 
0.1%
0.33 57
 
0.2%
0.4 4
 
< 0.1%
0.5 26
 
0.1%
0.6 4
 
< 0.1%
0.67 62
 
0.2%
ValueCountFrequency (%)
52 11
< 0.1%
50 1
 
< 0.1%
49 5
< 0.1%
48 2
 
< 0.1%
47 4
 
< 0.1%
45 1
 
< 0.1%
44.75 1
 
< 0.1%
42 1
 
< 0.1%
40 2
 
< 0.1%
39 4
 
< 0.1%

SSTA_DHW
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1621
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0387102
Minimum0
Maximum53.6
Zeros12022
Zeros (%)35.0%
Negative0
Negative (%)0.0%
Memory size268.8 KiB
2024-06-27T12:24:28.647671image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.35
Q34.31
95-th percentile10.8
Maximum53.6
Range53.6
Interquartile range (IQR)4.31

Descriptive statistics

Standard deviation4.6351054
Coefficient of variation (CV)1.5253529
Kurtosis25.018058
Mean3.0387102
Median Absolute Deviation (MAD)1.35
Skewness3.9162547
Sum104510.36
Variance21.484202
MonotonicityNot monotonic
2024-06-27T12:24:28.693685image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12022
35.0%
1 232
 
0.7%
1.05 206
 
0.6%
1.02 196
 
0.6%
1.08 192
 
0.6%
1.06 178
 
0.5%
1.03 177
 
0.5%
1.01 171
 
0.5%
1.12 160
 
0.5%
1.04 152
 
0.4%
Other values (1611) 20707
60.2%
ValueCountFrequency (%)
0 12022
35.0%
0.13 8
 
< 0.1%
0.15 4
 
< 0.1%
0.16 10
 
< 0.1%
0.17 15
 
< 0.1%
0.19 5
 
< 0.1%
0.2 6
 
< 0.1%
0.21 20
 
0.1%
0.23 5
 
< 0.1%
0.25 54
 
0.2%
ValueCountFrequency (%)
53.6 1
 
< 0.1%
52.8 1
 
< 0.1%
51.11 6
< 0.1%
50.31 1
 
< 0.1%
49.99 1
 
< 0.1%
49.32 1
 
< 0.1%
49.3 1
 
< 0.1%
48.55 1
 
< 0.1%
47.73 1
 
< 0.1%
47.69 2
 
< 0.1%

TSA
Real number (ℝ)

HIGH CORRELATION 

Distinct1040
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.96135347
Minimum-11.97
Maximum5.9
Zeros158
Zeros (%)0.5%
Negative24481
Negative (%)71.2%
Memory size268.8 KiB
2024-06-27T12:24:28.741805image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-11.97
5-th percentile-4.02
Q1-1.81
median-0.71
Q30.13
95-th percentile1.23
Maximum5.9
Range17.87
Interquartile range (IQR)1.94

Descriptive statistics

Standard deviation1.6480858
Coefficient of variation (CV)-1.7143391
Kurtosis2.632619
Mean-0.96135347
Median Absolute Deviation (MAD)0.93
Skewness-1.0063621
Sum-33063.83
Variance2.716187
MonotonicityNot monotonic
2024-06-27T12:24:28.784378image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.59 241
 
0.7%
-0.14 213
 
0.6%
0.14 205
 
0.6%
-1.06 199
 
0.6%
-1.19 192
 
0.6%
-0.53 191
 
0.6%
-0.29 178
 
0.5%
0.29 173
 
0.5%
-0.91 169
 
0.5%
0.53 167
 
0.5%
Other values (1030) 32465
94.4%
ValueCountFrequency (%)
-11.97 1
 
< 0.1%
-11.65 1
 
< 0.1%
-11.04 2
< 0.1%
-11.03 2
< 0.1%
-11.02 3
< 0.1%
-10.9 1
 
< 0.1%
-10.88 1
 
< 0.1%
-10.84 1
 
< 0.1%
-10.83 2
< 0.1%
-10.66 4
< 0.1%
ValueCountFrequency (%)
5.9 1
 
< 0.1%
5.37 1
 
< 0.1%
5.24 1
 
< 0.1%
5.1 3
< 0.1%
5.06 3
< 0.1%
4.92 2
< 0.1%
4.85 1
 
< 0.1%
4.83 1
 
< 0.1%
4.81 2
< 0.1%
4.79 2
< 0.1%

TSA_Frequency
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct197
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0827395
Minimum0
Maximum29
Zeros11111
Zeros (%)32.3%
Negative0
Negative (%)0.0%
Memory size268.8 KiB
2024-06-27T12:24:28.830914image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile7
Maximum29
Range29
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.0108187
Coefficient of variation (CV)1.445605
Kurtosis20.823498
Mean2.0827395
Median Absolute Deviation (MAD)1
Skewness3.7768935
Sum71631.66
Variance9.065029
MonotonicityNot monotonic
2024-06-27T12:24:28.873958image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11111
32.3%
1 6042
17.6%
2 4575
13.3%
3 3179
 
9.2%
4 2271
 
6.6%
5 1038
 
3.0%
6 818
 
2.4%
7 551
 
1.6%
8 356
 
1.0%
0.33 258
 
0.8%
Other values (187) 4194
 
12.2%
ValueCountFrequency (%)
0 11111
32.3%
0.12 17
 
< 0.1%
0.14 10
 
< 0.1%
0.17 42
 
0.1%
0.2 28
 
0.1%
0.25 169
 
0.5%
0.29 14
 
< 0.1%
0.33 258
 
0.8%
0.38 22
 
0.1%
0.4 25
 
0.1%
ValueCountFrequency (%)
29 2
 
< 0.1%
28 1
 
< 0.1%
27 2
 
< 0.1%
26.5 1
 
< 0.1%
26 5
 
< 0.1%
25 45
0.1%
24 42
0.1%
23.8 1
 
< 0.1%
23 43
0.1%
22 41
0.1%

TSA_DHW
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1119
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2716096
Minimum0
Maximum52.45
Zeros23322
Zeros (%)67.8%
Negative0
Negative (%)0.0%
Memory size268.8 KiB
2024-06-27T12:24:28.919053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.19
95-th percentile6.13
Maximum52.45
Range52.45
Interquartile range (IQR)1.19

Descriptive statistics

Standard deviation3.5796208
Coefficient of variation (CV)2.8150312
Kurtosis66.746043
Mean1.2716096
Median Absolute Deviation (MAD)0
Skewness7.0039673
Sum43734.47
Variance12.813685
MonotonicityNot monotonic
2024-06-27T12:24:28.964254image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23322
67.8%
1.06 172
 
0.5%
1.01 138
 
0.4%
1 135
 
0.4%
1.03 112
 
0.3%
1.07 109
 
0.3%
1.2 108
 
0.3%
1.02 106
 
0.3%
1.18 106
 
0.3%
1.04 105
 
0.3%
Other values (1109) 9980
29.0%
ValueCountFrequency (%)
0 23322
67.8%
0.13 10
 
< 0.1%
0.14 1
 
< 0.1%
0.17 5
 
< 0.1%
0.18 1
 
< 0.1%
0.19 8
 
< 0.1%
0.2 7
 
< 0.1%
0.21 7
 
< 0.1%
0.22 1
 
< 0.1%
0.23 8
 
< 0.1%
ValueCountFrequency (%)
52.45 1
 
< 0.1%
50.63 6
< 0.1%
49.4 1
 
< 0.1%
48.96 1
 
< 0.1%
48.9 1
 
< 0.1%
47.52 2
 
< 0.1%
47.45 1
 
< 0.1%
46.99 1
 
< 0.1%
46.81 2
 
< 0.1%
46.79 1
 
< 0.1%

Date
Date

Distinct4405
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Memory size268.8 KiB
Minimum1983-01-15 00:00:00
Maximum2019-12-22 00:00:00
2024-06-27T12:24:29.009111image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:29.058751image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Temperature_C
Real number (ℝ)

HIGH CORRELATION 

Distinct1142
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.225385
Minimum13.89
Maximum37.29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size268.8 KiB
2024-06-27T12:24:29.105845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum13.89
5-th percentile24.57
Q127.18
median28.56
Q329.59
95-th percentile30.79
Maximum37.29
Range23.4
Interquartile range (IQR)2.41

Descriptive statistics

Standard deviation1.9805506
Coefficient of variation (CV)0.070169126
Kurtosis2.1614418
Mean28.225385
Median Absolute Deviation (MAD)1.17
Skewness-1.0366511
Sum970755.68
Variance3.9225808
MonotonicityNot monotonic
2024-06-27T12:24:29.149770image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.02 173
 
0.5%
29.82 167
 
0.5%
28.77 154
 
0.4%
28.2 152
 
0.4%
28.89 145
 
0.4%
28.11 137
 
0.4%
29.45 137
 
0.4%
29.33 136
 
0.4%
28.42 136
 
0.4%
28.67 134
 
0.4%
Other values (1132) 32922
95.7%
ValueCountFrequency (%)
13.89 1
 
< 0.1%
14.03 1
 
< 0.1%
14.48 1
 
< 0.1%
14.58 1
 
< 0.1%
14.61 1
 
< 0.1%
15.11 1
 
< 0.1%
15.68 1
 
< 0.1%
16.17 1
 
< 0.1%
16.38 3
< 0.1%
16.51 1
 
< 0.1%
ValueCountFrequency (%)
37.29 1
< 0.1%
37.11 1
< 0.1%
36.84 1
< 0.1%
36.67 1
< 0.1%
36.61 1
< 0.1%
36.56 1
< 0.1%
36.05 1
< 0.1%
35.66 1
< 0.1%
34.99 1
< 0.1%
34.61 1
< 0.1%

Temperature_Maximum_C
Real number (ℝ)

Distinct632
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.984561
Minimum27.23
Maximum39.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size268.8 KiB
2024-06-27T12:24:29.192412image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum27.23
5-th percentile29.866
Q131.27
median31.95
Q332.63
95-th percentile33.77
Maximum39.99
Range12.76
Interquartile range (IQR)1.36

Descriptive statistics

Standard deviation1.3025697
Coefficient of variation (CV)0.040724952
Kurtosis4.7953515
Mean31.984561
Median Absolute Deviation (MAD)0.68
Skewness0.97306042
Sum1100045
Variance1.6966878
MonotonicityNot monotonic
2024-06-27T12:24:29.240303image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.45 581
 
1.7%
31.24 467
 
1.4%
31.83 399
 
1.2%
31.14 382
 
1.1%
31.47 361
 
1.0%
32.37 345
 
1.0%
32.45 333
 
1.0%
32.32 330
 
1.0%
32.33 328
 
1.0%
33.05 307
 
0.9%
Other values (622) 30560
88.9%
ValueCountFrequency (%)
27.23 2
 
< 0.1%
27.33 1
 
< 0.1%
27.81 1
 
< 0.1%
27.89 1
 
< 0.1%
28.02 1
 
< 0.1%
28.07 1
 
< 0.1%
28.1 2
 
< 0.1%
28.17 2
 
< 0.1%
28.24 8
< 0.1%
28.27 5
< 0.1%
ValueCountFrequency (%)
39.99 28
 
0.1%
39.61 1
 
< 0.1%
38.45 1
 
< 0.1%
38.01 14
 
< 0.1%
37.99 1
 
< 0.1%
37.98 8
 
< 0.1%
37.95 3
 
< 0.1%
37.93 9
 
< 0.1%
37.89 1
 
< 0.1%
37.86 84
0.2%

Bleaching_indicator
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size268.8 KiB
0
24249 
1
10144 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters34393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 24249
70.5%
1 10144
29.5%

Length

2024-06-27T12:24:29.281176image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-27T12:24:29.316892image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 24249
70.5%
1 10144
29.5%

Most occurring characters

ValueCountFrequency (%)
0 24249
70.5%
1 10144
29.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34393
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 24249
70.5%
1 10144
29.5%

Most occurring scripts

ValueCountFrequency (%)
Common 34393
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 24249
70.5%
1 10144
29.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 24249
70.5%
1 10144
29.5%

Exposure_cat
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size268.8 KiB
0.0
19394 
1.0
12225 
0.5
2774 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters103179
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 19394
56.4%
1.0 12225
35.5%
0.5 2774
 
8.1%

Length

2024-06-27T12:24:29.347178image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-27T12:24:29.384321image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 19394
56.4%
1.0 12225
35.5%
0.5 2774
 
8.1%

Most occurring characters

ValueCountFrequency (%)
0 53787
52.1%
. 34393
33.3%
1 12225
 
11.8%
5 2774
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68786
66.7%
Other Punctuation 34393
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 53787
78.2%
1 12225
 
17.8%
5 2774
 
4.0%
Other Punctuation
ValueCountFrequency (%)
. 34393
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 103179
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 53787
52.1%
. 34393
33.3%
1 12225
 
11.8%
5 2774
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 103179
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 53787
52.1%
. 34393
33.3%
1 12225
 
11.8%
5 2774
 
2.7%

Interactions

2024-06-27T12:24:24.039581image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:06.525924image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:07.462007image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:08.346650image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:09.193628image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:10.112085image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:10.963342image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:11.897372image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:12.695039image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:13.512001image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:14.493231image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:15.359170image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:16.187734image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:17.186263image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:18.030913image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:18.843234image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:19.694568image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:20.510967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:21.573600image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:22.414485image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:23.227775image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:24.078568image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:06.613383image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:07.499482image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:08.385343image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:09.232462image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:10.151192image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:11.001002image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:11.933980image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:12.735721image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:13.549994image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:14.533641image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:15.397387image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:16.226469image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:17.225790image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:18.069076image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:18.882669image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:19.733002image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:20.550719image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:21.612876image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:22.452829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:23.265725image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:24.118369image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:06.680145image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:07.536371image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:08.426925image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:09.365326image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:10.191319image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:11.039247image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:11.971448image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:12.774275image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:13.589275image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:14.574880image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:15.437097image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:16.265235image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:17.266378image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:18.107443image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:18.922265image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:19.772247image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:20.794697image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:21.652432image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:22.492205image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:23.304296image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:24.161145image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:06.747779image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:07.577713image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:08.467278image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:09.406605image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:10.234080image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:11.079853image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:12.010529image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:12.814651image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:13.629119image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:14.617781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:15.478335image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:16.306248image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:17.308254image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:18.147600image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:18.963802image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:19.816680image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:20.837890image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:21.694281image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:22.533296image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:23.344907image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:24.202098image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:06.802465image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:07.617094image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:08.507835image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:09.446546image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:10.274705image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:11.119999image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:12.049306image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:12.853579image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:13.805176image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:14.660204image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:15.517778image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:16.345163image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:17.349432image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:18.187454image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:19.003933image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:19.856371image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:20.880211image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:21.735095image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:22.572316image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:23.383917image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:24.244630image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:06.844044image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:07.735254image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:08.548242image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:09.487275image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:10.314114image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:11.159763image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:12.089356image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:12.893069image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:13.847714image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:14.702312image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:15.561104image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:16.385719image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:17.390845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:18.227500image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:19.044889image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:19.897178image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:20.922795image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:21.776466image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:22.612384image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:23.423576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:24.285187image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:06.882094image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:07.774548image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:08.589298image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:09.526801image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:10.359910image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:11.202100image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:12.127404image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:12.931568image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:13.899456image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:14.744590image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:15.600466image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:16.425761image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:17.431295image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:18.267539image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:19.084185image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:19.936214image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:20.964493image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:21.816661image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:22.651867image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:23.462703image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:24.323171image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:06.918453image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:07.810909image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:08.626569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:09.563917image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:10.397578image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:11.350049image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:12.161608image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:12.967498image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:13.941234image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:14.783050image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:15.643080image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:16.467292image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:17.469226image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:18.303233image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:19.121834image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:19.973332image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:21.003671image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:21.854395image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:22.687922image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:23.499231image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:24.363584image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:06.957907image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:07.851370image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:08.665917image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:09.602597image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:10.437977image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:11.389333image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:12.199532image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:13.006069image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:13.981077image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:14.825101image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:15.681761image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:16.512264image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:17.509788image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:18.342465image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:19.160356image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:20.012757image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:21.044134image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:21.894343image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:22.727210image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:23.537431image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:24.403076image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:06.995793image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:07.888878image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:08.704023image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:09.640553image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:10.478681image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:11.426973image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:12.235397image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:13.043025image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:14.018631image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:14.864228image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:15.719733image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:16.550314image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:17.548203image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:18.379032image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:19.198304image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:20.049949image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:21.083239image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:21.933742image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:22.764185image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:23.575205image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:24.446533image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:07.036670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:07.929573image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:08.749685image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:09.682263image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:10.520090image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:11.469238image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:12.277310image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:13.083920image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:14.060525image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:14.910069image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:15.763666image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:16.593269image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:17.590907image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:18.420245image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:19.241485image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:20.090317image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:21.125544image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:21.975890image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:22.805535image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:23.616346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:24.485930image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:07.073582image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:07.966768image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:08.790569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:09.719922image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:10.560211image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:11.506487image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:12.316614image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:13.120690image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:14.098421image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:14.951513image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:15.801698image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:16.633140image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:17.630176image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:18.458256image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:19.283798image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:20.128186image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:21.165318image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:22.014541image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:22.843178image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:23.656500image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:24.524835image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:07.111135image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:08.003862image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:08.830620image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:09.760001image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:10.599186image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:11.544252image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:12.352589image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:13.158403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:14.135741image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:14.991959image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:15.839599image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:16.670833image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:17.669794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:18.495533image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:19.329991image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:20.165554image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:21.204711image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:22.053791image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:22.881441image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:23.694127image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:24.567132image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:07.150902image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:08.043722image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:08.871124image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:09.800387image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:10.640207image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:11.589786image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:12.391401image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:13.198989image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:14.175553image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:15.034461image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:15.879775image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:16.710593image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:17.710534image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:18.535279image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:19.381654image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:20.205330image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:21.247089image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:22.095132image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:22.921356image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:23.733675image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:24.606291image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:07.187487image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:08.080082image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:08.910790image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:09.837913image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:10.678054image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:11.626975image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:12.429142image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:13.237017image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:14.213092image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:15.074044image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:15.917058image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:16.748386image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:17.750027image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:18.571600image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:19.420711image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:20.243215image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:21.285726image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:22.133100image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:22.958913image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:23.771437image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:24.647505image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:07.225781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:08.117465image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:08.952154image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:09.875807image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:10.716763image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:11.664460image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:12.465495image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:13.275346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:14.251447image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:15.114058image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:15.954778image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:16.950126image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:17.790033image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:18.612173image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:19.458275image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:20.280321image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:21.326127image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:22.170863image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:22.996982image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:23.808968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:24.687469image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:07.262081image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:08.154264image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:08.990209image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:09.914774image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:10.754588image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:11.701883image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:12.501268image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:13.313246image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:14.288964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:15.153872image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:15.991818image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:16.988563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:17.829806image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:18.650022image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:19.495739image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:20.316799image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:21.364668image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:22.209811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:23.033914image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:23.845630image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:24.729640image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:07.302047image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:08.194719image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:09.031908image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:09.955810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:10.796556image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:11.742066image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:12.540834image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:13.355086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:14.336238image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:15.196605image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:16.032301image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:17.030750image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:17.871503image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:18.691229image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:19.537568image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:20.358483image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:21.406836image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:22.251270image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:23.075348image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:23.886931image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:24.771063image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:07.343378image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:08.233169image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:09.072173image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:09.996572image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:10.836392image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:11.781318image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:12.580663image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:13.395457image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:14.378969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:15.237976image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:16.070419image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:17.071037image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:17.912961image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:18.729363image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:19.576875image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:20.397615image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:21.448423image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:22.291049image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:23.113437image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:23.925011image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:24.810518image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:07.380490image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:08.270045image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:09.110404image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:10.034400image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:10.881098image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:11.819028image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:12.616441image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:13.432911image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:14.416326image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:15.277963image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:16.107178image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:17.109178image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:17.951724image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:18.766654image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:19.615742image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:20.434272image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:21.491423image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:22.328883image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:23.151012image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:23.964205image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:24.849838image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:07.418033image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:08.307473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:09.149026image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:10.071520image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:10.918291image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:11.856267image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:12.652006image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:13.469999image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:14.453124image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:15.316277image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:16.148228image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:17.145809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:17.989851image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:18.803480image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:19.653785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:20.471277image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:21.530704image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:22.367024image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:23.187683image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-27T12:24:24.000122image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2024-06-27T12:24:29.427543image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Bleaching_indicatorCyclone_FrequencyData_SourceDate_DayDate_MonthDate_YearDepth_mDistance_to_ShoreExposureExposure_catLatitude_DegreesLongitude_DegreesOcean_NamePercent_BleachingRealm_NameSSTASSTA_DHWSSTA_FrequencySample_IDSite_IDSubstrate_NameTSATSA_DHWTSA_FrequencyTemperature_CTemperature_Maximum_CTurbidityWindspeed
Bleaching_indicator1.0000.0400.691-0.0370.032-0.2180.1500.2360.2240.2240.170-0.2250.3240.8380.3220.1200.2170.1160.1870.0650.0200.1560.2980.1460.113-0.059-0.0550.042
Cyclone_Frequency0.0401.0000.168-0.0090.126-0.0300.0380.0930.1620.1620.423-0.0960.2830.0520.191-0.0200.0070.021-0.0680.2570.0420.0760.028-0.0090.098-0.0190.0030.046
Data_Source0.6910.1681.0000.036-0.0140.525-0.134-0.3410.2950.295-0.3780.4330.349-0.5440.284-0.013-0.0730.088-0.516-0.3871.000-0.094-0.158-0.013-0.0570.0630.092-0.069
Date_Day-0.037-0.0090.0361.000-0.0720.010-0.012-0.0260.1460.1460.0070.0010.118-0.0370.091-0.001-0.007-0.016-0.018-0.0040.000-0.038-0.029-0.032-0.0090.0380.0200.008
Date_Month0.0320.126-0.014-0.0721.0000.0210.036-0.0160.1260.1260.122-0.0460.1680.0370.1170.0180.1040.018-0.0670.0710.0260.0760.114-0.0170.097-0.057-0.021-0.020
Date_Year-0.218-0.0300.5250.0100.0211.000-0.036-0.2040.1470.147-0.1060.1870.192-0.2360.1840.0800.0590.271-0.536-0.0490.1880.036-0.0400.0560.1180.0610.108-0.190
Depth_m0.1500.038-0.134-0.0120.036-0.0361.0000.1350.2140.2140.142-0.0630.1610.1660.1610.0020.0090.0360.1170.1020.0220.0350.0580.0770.058-0.054-0.1380.004
Distance_to_Shore0.2360.093-0.341-0.026-0.016-0.2040.1351.0000.0530.0530.041-0.1560.0580.2800.2910.010-0.015-0.1190.1380.1130.0130.0270.035-0.0520.0400.0480.0650.048
Exposure0.2240.1620.2950.1460.1260.1470.2140.0531.0001.000-0.1780.1370.274-0.0730.288-0.0040.0020.0300.008-0.1060.026-0.014-0.0080.0530.0020.0550.106-0.032
Exposure_cat0.2240.1620.2950.1460.1260.1470.2140.0531.0001.0000.280-0.2740.2740.1950.2880.0180.007-0.0380.1370.1700.0260.0340.009-0.0350.021-0.095-0.2190.035
Latitude_Degrees0.1700.423-0.3780.0070.122-0.1060.1420.041-0.1780.2801.000-0.4660.4380.1470.5020.0570.0860.0790.0690.3940.0640.0590.0410.0130.1010.035-0.0470.002
Longitude_Degrees-0.225-0.0960.4330.001-0.0460.187-0.063-0.1560.137-0.274-0.4661.0000.784-0.2440.6310.0130.0740.136-0.355-0.2800.072-0.0610.0410.070-0.0860.0350.319-0.015
Ocean_Name0.3240.2830.3490.1180.1680.1920.1610.0580.2740.2740.4380.7841.000-0.3610.6880.0180.0450.124-0.452-0.3780.026-0.0150.0080.062-0.0670.0200.114-0.159
Percent_Bleaching0.8380.052-0.544-0.0370.037-0.2360.1660.280-0.0730.1950.147-0.244-0.3611.0000.1160.1110.2060.0960.2290.0800.0000.1630.2930.1420.120-0.046-0.0280.067
Realm_Name0.3220.1910.2840.0910.1170.1840.1610.2910.2880.2880.5020.6310.6880.1161.0000.0370.005-0.0580.4660.2220.038-0.044-0.009-0.030-0.092-0.206-0.2030.188
SSTA0.120-0.020-0.013-0.0010.0180.0800.0020.010-0.0040.0180.0570.0130.0180.1110.0371.0000.3440.205-0.035-0.0190.0200.6040.2130.1450.478-0.0380.025-0.170
SSTA_DHW0.2170.007-0.073-0.0070.1040.0590.009-0.0150.0020.0070.0860.0740.0450.2060.0050.3441.0000.544-0.061-0.0650.0000.3160.6440.4020.2410.0080.119-0.047
SSTA_Frequency0.1160.0210.088-0.0160.0180.2710.036-0.1190.030-0.0380.0790.1360.1240.096-0.0580.2050.5441.000-0.192-0.0800.0140.1310.3480.5980.1090.0380.169-0.059
Sample_ID0.187-0.068-0.516-0.018-0.067-0.5360.1170.1380.0080.1370.069-0.355-0.4520.2290.466-0.035-0.061-0.1921.0000.0761.000-0.063-0.0110.011-0.153-0.177-0.2860.273
Site_ID0.0650.257-0.387-0.0040.071-0.0490.1020.113-0.1060.1700.394-0.280-0.3780.0800.222-0.019-0.065-0.0800.0761.0001.0000.101-0.013-0.0560.2010.0970.024-0.106
Substrate_Name0.0200.0421.0000.0000.0260.1880.0220.0130.0260.0260.0640.0720.0260.0000.0380.0200.0000.0141.0001.0001.0000.0060.0010.0120.0100.006-0.008-0.008
TSA0.1560.076-0.094-0.0380.0760.0360.0350.027-0.0140.0340.059-0.061-0.0150.163-0.0440.6040.3160.131-0.0630.1010.0061.0000.3570.1390.8460.0870.026-0.347
TSA_DHW0.2980.028-0.158-0.0290.114-0.0400.0580.035-0.0080.0090.0410.0410.0080.293-0.0090.2130.6440.348-0.011-0.0130.0010.3571.0000.5380.2610.0350.080-0.030
TSA_Frequency0.146-0.009-0.013-0.032-0.0170.0560.077-0.0520.053-0.0350.0130.0700.0620.142-0.0300.1450.4020.5980.011-0.0560.0120.1390.5381.0000.0690.0350.0290.030
Temperature_C0.1130.098-0.057-0.0090.0970.1180.0580.0400.0020.0210.101-0.086-0.0670.120-0.0920.4780.2410.109-0.1530.2010.0100.8460.2610.0691.0000.4290.159-0.498
Temperature_Maximum_C-0.059-0.0190.0630.038-0.0570.061-0.0540.0480.055-0.0950.0350.0350.020-0.046-0.206-0.0380.0080.038-0.1770.0970.0060.0870.0350.0350.4291.0000.382-0.323
Turbidity-0.0550.0030.0920.020-0.0210.108-0.1380.0650.106-0.219-0.0470.3190.114-0.028-0.2030.0250.1190.169-0.2860.024-0.0080.0260.0800.0290.1590.3821.000-0.236
Windspeed0.0420.046-0.0690.008-0.020-0.1900.0040.048-0.0320.0350.002-0.015-0.1590.0670.188-0.170-0.047-0.0590.273-0.106-0.008-0.347-0.0300.030-0.498-0.323-0.2361.000

Missing values

2024-06-27T12:24:24.955942image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-27T12:24:25.190931image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-06-27T12:24:25.382977image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Site_IDSample_IDData_SourceLatitude_DegreesLongitude_DegreesOcean_NameReef_IDRealm_NameEcoregion_NameCountry_NameState_Island_Province_NameCity_Town_NameDistance_to_ShoreExposureTurbidityCyclone_FrequencyDate_DayDate_MonthDate_YearDepth_mSubstrate_NamePercent_BleachingWindspeedSSTASSTA_FrequencySSTA_DHWTSATSA_FrequencyTSA_DHWDateTemperature_CTemperature_Maximum_CBleaching_indicatorExposure_cat
0250110324336Donner23.1630-82.5260AtlanticNaNTropical AtlanticCuba and Cayman IslandsCubaHavanaHavana8519.23Exposed0.028749.90159200510.00NaN50.208.0-0.460.00.00-0.800.000.002005-09-1528.9031.5411.0
1346710324754Donner-17.5750-149.7833PacificNaNEastern Indo-PacificSociety Islands French PolynesiaFrench PolynesiaSociety IslandsMoorea1431.62Exposed0.026251.20153199114.00NaN50.702.01.290.50.261.290.250.261991-03-1530.1531.8611.0
2179410323866Donner18.3690-64.5640AtlanticNaNTropical AtlanticHispaniola Puerto Rico and Lesser AntillesUnited KingdomBritish Virgin IslandsPeter Island182.33Exposed0.042961.5215120067.00NaN50.908.00.0416.00.00-2.647.000.002006-01-1526.0330.9911.0
3864710328028Donner17.7600-64.5680AtlanticNaNTropical AtlanticHispaniola Puerto Rico and Lesser AntillesUnited StatesUS Virgin IslandsSt. Croix313.13Exposed0.042465.3915420069.02NaN50.903.0-0.0722.00.00-2.273.000.002006-04-1526.4630.9211.0
4864810328029Donner17.7690-64.5830AtlanticNaNTropical AtlanticHispaniola Puerto Rico and Lesser AntillesUnited StatesUS Virgin IslandsSt. Croix792.00Exposed0.042465.39154200612.50NaN50.903.00.0016.00.00-2.193.000.002006-04-1526.5530.6111.0
5218010324021Donner9.8220-75.8910AtlanticNaNTropical AtlanticNetherlands Antilles and south CaribbeanColombiaSucreSan Bernardo Islands4569.60Sometimes0.095451.54158200511.50NaN51.001.00.275.04.280.172.002.212005-08-1530.0732.3310.5
6929810328657Donner17.8110-64.6300AtlanticNaNTropical AtlanticHispaniola Puerto Rico and Lesser AntillesUnited StatesUS Virgin IslandsSt. Croix2399.36Exposed0.037265.391510200527.70NaN51.306.00.2919.011.610.256.006.892005-10-1528.9730.7111.0
7537110325106Donner-3.990039.7500IndianNaNWestern Indo-PacificKenya and Tanzania coastKenyaMombasa CountyMombasa Marine Park1004.93Sheltered0.043242.611551998NaNNaN51.387.00.9115.014.760.179.0012.331998-05-1528.7331.1510.0
8916010328498Donner25.4250-80.1610AtlanticNaNTropical AtlanticBahamas and Florida KeysUnited StatesFloridaMiami-Dade County3920.39Sometimes0.163658.0115920054.05NaN51.408.00.353.01.53-0.570.000.002005-09-1529.4732.4010.5
949910322424Donner-19.1975146.8150PacificNaNCentral Indo-PacificCentral and northern Great Barrier ReefAustraliaQueenslandMiddle Reef2750.37Sheltered0.219743.521531998NaNNaN51.534.00.637.010.11-0.175.007.961998-03-1529.2632.9910.0
Site_IDSample_IDData_SourceLatitude_DegreesLongitude_DegreesOcean_NameReef_IDRealm_NameEcoregion_NameCountry_NameState_Island_Province_NameCity_Town_NameDistance_to_ShoreExposureTurbidityCyclone_FrequencyDate_DayDate_MonthDate_YearDepth_mSubstrate_NamePercent_BleachingWindspeedSSTASSTA_FrequencySSTA_DHWTSATSA_FrequencyTSA_DHWDateTemperature_CTemperature_Maximum_CBleaching_indicatorExposure_cat
34383280910319118Reef_Check19.0544-68.9616Atlantic68.57.41.6W.19.3.15.7NTropical AtlanticHispaniola Puerto Rico and Lesser AntillesDominican RepublicEl Seibo ProvinceMiches2708.99Exposed0.071255.92512200913.2Nutrient Indicator Algae100.07.00.579.06.16-0.993.03.592009-12-0527.8632.0111.0
34384357710314534Reef_Check-16.5427-151.7408Pacific151.44.447W.16.32.562SEastern Indo-PacificSociety Islands French PolynesiaFrench PolynesiaSociety IslandsBora Bora357.92Sheltered0.027852.335520192.0Hard Coral100.05.00.526.02.58-0.111.01.362019-05-0528.8231.6210.0
34385357710314534Reef_Check-16.5427-151.7408Pacific151.44.447W.16.32.562SEastern Indo-PacificSociety Islands French PolynesiaFrench PolynesiaSociety IslandsBora Bora357.92Sheltered0.027852.335520192.0Nutrient Indicator Algae100.05.00.526.02.58-0.111.01.362019-05-0528.8231.6210.0
34386831210321370Reef_Check10.116799.8444Pacific99.50.40E.10.7.0NCentral Indo-PacificGulf of ThailandThailandSurat ThaniKo Pha-ngan District73.88Exposed0.056550.2727520145.0Hard Coral100.02.01.402.01.371.261.01.332014-05-2731.6433.7411.0
34387831210321370Reef_Check10.116799.8444Pacific99.50.40E.10.7.0NCentral Indo-PacificGulf of ThailandThailandSurat ThaniKo Pha-ngan District73.88Exposed0.056550.2727520145.0Nutrient Indicator Algae100.02.01.402.01.371.261.01.332014-05-2731.6433.7411.0
34388943610318964Reef_Check18.3350-64.8486Atlantic64.50.55W.18.20.06NTropical AtlanticHispaniola Puerto Rico and Lesser AntillesUnited StatesUS Virgin IslandsSt Thomas49.16Sheltered0.058685.57231020053.0Hard Coral100.08.00.3212.010.140.215.06.222005-10-2328.9630.5210.0
34389943610318964Reef_Check18.3350-64.8486Atlantic64.50.55W.18.20.06NTropical AtlanticHispaniola Puerto Rico and Lesser AntillesUnited StatesUS Virgin IslandsSt Thomas49.16Sheltered0.058685.57231020053.0Nutrient Indicator Algae100.08.00.3212.010.140.215.06.222005-10-2328.9630.5210.0
34390983510290574McClanahan-13.500247.8825IndianNaNWestern Indo-PacificNorth MadagascarMadagascarAntsiranana ProvinceNaN8768.03Sometimes0.062835.71184201614.0NaN100.05.00.639.05.810.594.04.442016-04-1830.3032.1710.5
343911222810274702FRRP24.5019-81.6328AtlanticNaNTropical AtlanticBahamas and Florida KeysUnited StatesFloridaMonroe County8170.00Exposed0.120358.4210920154.0NaN100.05.03.9125.051.113.9125.050.632015-09-1030.6132.8911.0
343921305710275554FRRP24.9542-80.5458AtlanticNaNTropical AtlanticBahamas and Florida KeysUnited StatesFloridaMonroe County1863.00Sheltered0.170362.5471020154.0NaN100.02.01.5922.010.59-0.575.07.162015-10-0729.5533.6710.0